U.S. patent application number 14/511047 was filed with the patent office on 2016-04-14 for multivariate insight discovery approach.
The applicant listed for this patent is Julian Gosper, Alexander Kennedy MacAulay, Flavia Moser. Invention is credited to Julian Gosper, Alexander Kennedy MacAulay, Flavia Moser.
Application Number | 20160103902 14/511047 |
Document ID | / |
Family ID | 52272807 |
Filed Date | 2016-04-14 |
United States Patent
Application |
20160103902 |
Kind Code |
A1 |
Moser; Flavia ; et
al. |
April 14, 2016 |
Multivariate Insight Discovery Approach
Abstract
A raw dataset including measures and dimensions is processed, by
a preprocessing module, using an algorithm that produces a
preprocessed dataset such that at least one type of statistical
analysis of the preprocessed dataset yields equal results to the
same type of statistical analysis of the raw dataset. The
preprocessed dataset is then analyzed by a statistical analysis
module to identify subsets of the preprocessed dataset that include
a non-random structure or pattern. The analysis of the preprocessed
dataset includes the at least one type of statistical analysis that
produces the same results for both the preprocessed and raw
datasets. The identified subsets are then ranked by a statistical
ranker based on the analysis of the preprocessed dataset and a
subset is selected for visualization based on the rankings A
visualization module then generates a visualization of the selected
identified subset that highlights a non-random structure of the
selected subset.
Inventors: |
Moser; Flavia; (Vancouver,
CA) ; MacAulay; Alexander Kennedy; (Vancouver,
CA) ; Gosper; Julian; (Vancouver, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Moser; Flavia
MacAulay; Alexander Kennedy
Gosper; Julian |
Vancouver
Vancouver
Vancouver |
|
CA
CA
CA |
|
|
Family ID: |
52272807 |
Appl. No.: |
14/511047 |
Filed: |
October 9, 2014 |
Current U.S.
Class: |
707/602 ;
707/748 |
Current CPC
Class: |
G06F 16/254 20190101;
G06F 16/283 20190101; G06F 16/26 20190101; G06F 16/248 20190101;
G06T 11/206 20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06T 11/20 20060101 G06T011/20 |
Claims
1. A method comprising: accessing a dataset including measures and
dimensions by a preprocessing module including at least one
processor; processing the dataset, by the preprocessing module, to
generate a preprocessed dataset such that at least one type of
statistical analysis of the preprocessed dataset yields equal
results to the same type of statistical analysis of the dataset;
analyzing the preprocessed dataset, by a statistical analysis
module including at least one processor, to identify subsets of the
preprocessed dataset that include a non-random structure, the
analyzing including the at least one type of statistical analysis;
generating a score for each of the identified subsets, by the
statistical analysis module, based on the non-random structures
included in each of the identified subsets; ranking each of the
identified subsets, by a statistical ranking module including at
least one processor, based on the score generated for each of the
identified subsets and selecting an identified subset based on the
ranking of the identified subset; and generating, by a
visualization module including at least one processor, a
visualization that highlights a non-random structure of the
selected identified subset.
2. The method of claim 1, wherein the preprocessed dataset includes
at least one online analytical processing (OLAP) cube and
processing the dataset includes: discarding a measure of the
dataset based on more than half of the values of the measure being
zero; determining hierarchical relationships between the attributes
of the dataset; storing "Sum" and "Count" values for corresponding
measures of an attribute based on an aggregation type of the
attribute being "Average"; and aggregating over at least one
attribute of the dataset.
3. The method of claim 2, wherein processing the dataset includes
at least one of: aggregating over attributes of the dataset 99% of
the same value; aggregating over attributes of the dataset with a
cardinality greater than a threshold value; or aggregating over all
of the attributes of the dataset in order of decreasing cardinality
until the dataset has a threshold size.
4. The method of claim 3, wherein processing the dataset includes:
determining equivalencies between the attributes and between the
measures of the dataset; for equivalent attributes, discarding all
but one of the equivalent attributes; and for equivalent measures,
discarding all but one of the equivalent measures.
5. The method of claim 4, wherein the at least one type of
statistical analysis includes an analysis of variance (ANOVA) test,
the method further comprising: analyzing, by the statistical
analysis module, only subsets of the preprocessed dataset that
consist of an attribute and a measure and subsets that consist of
two attributes and a measure; generating, by the statistical
analysis module, a score for each identified subset based on an
effect size of ANOVA for the identified subset.
6. The method of claim 5, wherein the visualization includes at
least one of: a mark representing different values of an attribute,
a mark type for each type of data point representation, a mark
color property associated with a measure or an attribute, or a mark
size property associated with a value of a measure; the method
further comprising: selecting an attribute of the selected
identified subset for the color property based on attribute
hierarchies; and selecting a mark type based on a cardinality of an
attribute of the selected identified subset.
7. The method of claim 6, wherein the determining an attribute for
the color property based on the attribute hierarchies includes one
of: determining that an attribute is one of included as a mark and
included on an x-axis with a cardinality less than 10; or
determining that an attribute is at a higher hierarchy level than
an attribute used as a mark with a cardinality less than 10.
8. A system comprising: a preprocessing module including a
processor and configured to access a dataset including measures and
dimensions and process the dataset to generate a preprocessed
dataset such that at least one type of statistical analysis of the
preprocessed dataset yields equal results to the same type of
statistical analysis of the dataset; a statistical analysis module
including a processor and configured to: analyze the preprocessed
dataset to identify subsets of the preprocessed dataset that
include a non-random structure, the analyzing including the at
least one type of statistical analysis; and generate a score for
each of the identified subsets based on the non-random structures
included in each of the identified subsets; a statisitical ranking
module including a processor and configured to: rank each of the
identified subsets based on the score generated for each of the
identified subsets; and select an identified subset based on the
ranking of the identified subset; and a visualization module
including a processor and configured to generate a visualization of
the selected identified subset that highlights a non-random
structure of the selected identified subset.
9. The system of claim 8, wherein the preprocessed dataset includes
at least one online analytical processing (OLAP) cube and the
preprocessing module is configured to: discard a measure of the
dataset based on more than half of the values of the measure being
zero; determine hierarchical relationships between the attributes
of the dataset; store "Sum" and "Count" values for corresponding
measures of an attribute based on an aggregation type of the
attribute being "Average"; and aggregate over at least one
attribute of the dataset.
10. The system of claim 9, wherein the preprocessing module is
configured to perform at least one of: aggregate over attributes of
the dataset 99% of the same value; aggregate over attributes of the
dataset with a cardinality greater than a threshold value; or
aggregate over all of the attributes of the dataset in order of
decreasing cardinality until the dataset has a threshold size.
11. The system of claim 10, wherein the preprocessing module is
configured to: determining equivalencies between the attributes and
between the measures of the dataset; for equivalent attributes,
discarding all but one of the equivalent attributes; and for
equivalent measures, discarding all but one of the equivalent
measures.
12. The system of claim 11, wherein: the at least one type of
statistical analysis includes an analysis of variance (ANOVA) test;
and the statistical analysis module is configured to: analyze only
subsets of the preprocessed dataset that consist of an attribute
and a measure and subsets that consist of two attributes and a
measure; and generate a score for each identified subset based on
an effect size of ANOVA for the identified subset.
13. The system of claim 12, wherein the visualization includes at
least one of: a mark representing different values of an attribute,
a mark type for each type of data point representation, a mark
color property associated with a measure or an attribute, or a mark
size property associated with a value of a measure; and the
visualization module is further configured to: select an attribute
of the selected identified subset for the color property based on
attribute hierarchies; and select a mark type based on a
cardinality of an attribute of the selected identified subset.
14. The system of claim 13, wherein the determining an attribute
for the color property based on the attribute hierarchies includes
one of: determining that an attribute is one of included as a mark
or included on an x-axis with a cardinality less than 10; or
determining that an attribute is at a higher hierarchy level than
an attribute used as a mark with a cardinality less than 10.
15. A non-transitory machine-readable storage medium including
instructions that, when executed on at least one processor of a
machine, cause the machine to perform operations comprising:
accessing a dataset including measures and dimensions by a
preprocessing module; processing the dataset, by the preprocessing
module, to generate a preprocessed dataset such that at least one
type of statistical analysis of the preprocessed dataset yields
equal results to the same type of statistical analysis of the
dataset; analyzing the preprocessed dataset, by a statistical
analysis module, to identify subsets of the preprocessed dataset
that include a non-random structure, the analyzing including the at
least one type of statistical analysis; generating a score for each
of the identified subsets, by the statistical analysis module,
based on the non-random structures included in each of the
identified subsets; ranking each of the identified subsets, by a
statistical ranking, based on the score generated for each of the
identified subsets and selecting an identified subset based on the
ranking of the identified subset; and generating, by a
visualization module, a visualization that highlights a non-random
structure of the selected identified subset.
16. The machine-readable storage medium of claim 15, wherein the
preprocessed dataset includes at least one online analytical
processing (OLAP) cube and processing the dataset includes:
discarding a measure of the dataset based on more than half of the
values of the measure being zero; determining hierarchical
relationships between the attributes of the dataset; storing "Sum"
and "Count" values for corresponding measures of an attribute based
on an aggregation type of the attribute being "Average"; and
aggregating over at least one attribute of the dataset.
17. The machine-readable storage medium of claim 16, wherein
processing the dataset includes: determining equivalencies between
the attributes and between the measures of the dataset; for
equivalent attributes, discarding all but one of the equivalent
attributes; and for equivalent measures, discarding all but one of
the equivalent measures.
18. The machine-readable storage medium of claim 17, wherein the at
least one type of statistical analysis includes an analysis of
variance (ANOVA) test, the operations further comprising:
analyzing, by the statistical analysis module, only subsets of the
preprocessed dataset that consist of an attribute and a measure and
subsets that consist of two attributes and a measure; generating,
by the statistical analysis module, a score for each identified
subset based on an effect size of ANOVA for the identified
subset.
19. The machine-readable storage medium of claim 18, wherein the
visualization includes at least one of: a mark representing
different values of an attribute, a mark type for each type of data
point representation, a mark color property associated with a
measure or an attribute, or a mark size property associated with a
value of a measure; the operations further comprising: selecting an
attribute of the selected identified subset for the color property
based on attribute hierarchies; and selecting a mark type based on
a cardinality of an attribute of the selected identified
subset.
20. The machine-readable storage medium of claim 19, wherein the
determining an attribute for the color property based on attribute
hierarchies includes one of: determining that an attribute is one
of included as a mark and included on an x-axis with a cardinality
less than 10; or determining that an attribute is at a higher
hierarchy level than an attribute used as a mark with a cardinality
less than 10.
Description
TECHNICAL FIELD
[0001] This patent document relates generally to data analysis and
visualization, and more particularly, but not by way of limitation,
to systems and methods for automatically identifying subsets of a
dataset that are used to produce insightful visualizations for a
user who may have little or no experience with the particular
dataset.
BACKGROUND
[0002] In a typical business environment a user may be presented
with reports including data relating to subjects or fields that the
user may not be familiar with. A user who is not an expert in the
relevant area of a report may be overwhelmed when it comes to
discerning interesting relationships from the data of the report.
Furthermore, the user may desire an "insightful" visualization of
the report data, for example a visualization that presents the
subsets of data that exhibit a non-random and/or non-intuitive
structure to the user in a form that is clear and visually
appealing.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings in
which:
[0004] FIG. 1 is a visualization, according to an example
embodiment, of data subsets including non-random structures.
[0005] FIG. 2 is a block diagram illustrating a hardware
environment and data flow generating visualizations, according to
an example embodiment.
[0006] FIGS. 3A and 3B illustrate the disappearance and appearance
of patterns in data as a result of aggregation, according to an
example embodiment.
[0007] FIG. 4 includes a table illustrating visualization
guidelines, according to an example embodiment.
[0008] FIG. 5 illustrates attribute hierarchies of "online
analytical processing" (OLAP) data determined according to an
example embodiment.
[0009] FIG. 6 is a flowchart illustrating a method, according to an
example embodiment, for generating a visualization of a dataset
identified as including a non-random structure.
[0010] FIG. 7 is a flowchart illustrating a method, according to an
example embodiment, for generating a visualization of a dataset
identified as including a non-random structure.
[0011] FIG. 8 is a diagrammatic representation of a machine in the
example form of a computer system within which a set of
instructions for causing the machine to perform any one or more of
the methodologies discussed herein may be executed.
DETAILED DESCRIPTION
[0012] In example embodiments, the following terms may include the
meanings as shown below.
[0013] OLAP--Online Analytical Processing includes a field of study
that seeks to answer multi-dimensional analytic queries
swiftly.
[0014] Dataset--includes a collection of data, usually in a table
form. The table has m rows and n columns, where each column is a
variable in the data collection, and each row is a member of the
dataset across all columns.
[0015] Column--includes a variable in a dataset, consisting of a
list of values from the dataset.
[0016] Attribute--includes a column in a dataset, used
interchangeably with "dimension" in this document.
[0017] Measure--includes a numerical column in a dataset, often
used in aggregational calculations over dimensions.
[0018] Dimension--includes a categorical column in a dataset, often
used as an aggregator for numerical columns in the dataset called
measures.
[0019] Cube--includes a dataset with at least one dimension and at
least one measure used for data analysis.
[0020] OLAP cube--includes a multidimensional database that is
optimized for data warehouse and online analytical processing
(OLAP) applications. In OLAP cubes, data (measures) are categorized
by dimensions. OLAP cubes are often pre-summarized across
dimensions to drastically improve query time over relational
databases.
[0021] Example systems and methods are described for enabling
insightful visualizations of datasets even when, for example, a
user is unfamiliar with the subject matter of the dataset. Also
described by way of example are systems and methods for discovering
non-random patterns or structures in subsets of the dataset. In
some example embodiments, the systems and methods for enabling
insightful visualization of datasets include processing raw data
including dimensions and measures by aggregating over attributes of
the raw data, among other things. In the following description, for
purposes of explanation, numerous specific details are set forth in
order to provide a thorough understanding of example embodiments.
It will be evident, however, to one skilled in the art, that the
disclosed systems and methods may be practiced without these
specific details. It will also be evident that insightful
visualization of datasets is not limited to the examples provided
and may include other scenarios not specifically discussed.
[0022] In order to overcome the limitations of working with
datasets a user is unfamiliar with, a system may adopt a
preprocessing strategy that may create a preprocessed dataset that
has the same relevant statistical properties as the raw data of the
dataset. For example, the raw data may be sampled, projected or
aggregated; or non-useful or duplicative data of the raw data may
be discarded. The preprocessed dataset may then be analyzed to
identify subsets of relevant data (e.g., columns) that contain
insightful information, and after these subsets have been
identified they may then be ranked or sorted to determine which are
the most insightful or useful to a user. Still further, the system
may determine how to visualize the results of the statistical
analysis for a selected subset in a way that highlights its
determinations, for example by highlighting any non-random
structures or patterns in the data.
Example Visualization
[0023] FIG. 1 includes a visualization 100 generated according to
an example embodiment for enabling an insightful visualization of a
dataset. In an example, the multivariate insight discovery approach
is used to present insightful visualizations to a user who has
little or no experience with a particular data set. In this
example, the dataset includes OLAP data. Typically, an OLAP dataset
consists of columns that are classified as either measures or
attributes (e.g., dimensions), which may be part of a hierarchical
structure. In the case of a column classified as a measure, the
OLAP data also includes a proposed aggregation method for the
measure. An OLAP cube may be considered a generalization of a
three-dimensional spreadsheet. For example, a company might wish to
summarize financial data by product ID, by time-period, and by
location to compare the relevant data in each dimension. Product
ID, time, and location comparison are the data's dimensions. Each
cell of the cube holds a number that represents some measure of the
business, such as sales. The elements of a dimension may be
organized as a hierarchy, a set of parent-child relationships,
typically where a parent member summarizes or includes its
children. Furthermore, parent elements may be aggregated as the
children of another parent element of the OLAP data. For example, a
monthly report may be the child of a quarterly report which is in
turn the child of a yearly report, etc.
[0024] Before going into the details of the visualization
guidelines that may be used to produce visualization 100, some
concepts and their relation to the example visualizations herein
are now described:
[0025] x-axis/y-axis: a measure or an attribute may be associated
with an axis.
[0026] mark: represents the different values of the dimension
associated with each data point that is represented in the
visualization. For example, in the discussion of FIG. 1 (below),
each mark (i.e., circle) represents a different "destination" data
point.
[0027] mark type: the type of the data point representation, e.g.,
bar or dot.
[0028] color property: a measure or an attribute may be associated
with the color property; different values are represented by
different colors.
[0029] size property: the value of the measure associated with the
size property determines the size of the mark, e.g., dot size.
[0030] row/column: this feature may be used to create a trellis
chart.
[0031] visualization type: how the data points are displayed with
respect to each other, e.g., comparison, stack, spread, scatter,
group.
[0032] With these concepts in mind, an example set of visualization
guidelines for displaying a subset of a dataset and highlighting
the statistical insights found by analyzing the dataset,
particularly with respect to FIG. 1.
[0033] In the example of FIG. 1, the visualization 100 is generated
(after consulting appropriate visualization guidelines) for a set
of columns comprising an attribute and three measures (i.e.,
AM.sub.1M.sub.2M.sub.3). The visualization type is "scatter" and
the visualization 100 makes use of a mark 130 (circle) to represent
each data point in the attribute "destination"; in this case each
data point represents a location, e.g., airports. The visualization
100 of FIG. 1 also makes use of a color property to represent the
measure "average `Air Time` of each flight arriving at each
destination" by coloring each mark 130 darker as the average flight
time for airplanes arriving at the destination increases. The "Air
Time" legend 140 indicates the functioning of the color property.
It should be noted that a size property might also be used, for
example, each mark 130 may be shown as larger or smaller depending
on the volume of aircraft passing through the airport location each
day. Furthermore, the measure "security delay" 110 is shown along
the y-axis and the measure "weather delay" is shown along the
x-axis.
[0034] Since a dataset may be hard to interpret for an unfamiliar
user, the goal is to automatically identify subsets of the measures
and attributes of the OLAP data that result in insightful
visualizations for the viewing user. In the context of this patent
document, a visualization may be considered insightful or
interesting if the data included in the visualization has a
non-random structure. For example, a user may access a large volume
of data regarding delays and flight times from airports at multiple
locations and find it impossible to make any meaningful
observations regarding the dataset. However, according to example
methods and systems described herein, a visualization 100 of
airplane flight data, as illustrated in FIG. 1, may allow the user
to see that the airport security delays 110 (y-axis) are clearly
correlated with the weather delays 120 (x-axis) in a non-random
structure or pattern. Furthermore, a user may also see that the
color property (flight time) is also correlated with the delays as
is evident from the darker colors as the delays increase. These
relationships might be difficult to identify from a large set of
non-visual data.
[0035] In order to identify subsets of the dataset that may result
in insightful visualizations (i.e., include a non-random
structure), different types of statistical analysis methods may be
applied to the same level of aggregation of the OLAP data as the
one that will be shown in the visualization. The visualization of
these subsets may be used to communicate insightful information to
a viewing user by employing the tailored set of visualization
selection guidelines according to the embodiments described
herein.
Example Operating Environment
[0036] FIG. 2 is a block diagram illustrating an environment 200
for operating a visualization generator 204, according to an
example embodiment. The environment 200 is an example environment
within which methods for implementing insightful visualizations of
datasets may be performed for a raw dataset 212. The environment
200 may include a client device 202 (e.g., processing device such
as a desktop computer, laptop, tablet or smartphone), and elements
of a system for processing and visualizing datasets. The elements
may include a visualization generator 204, a statistical ranker
206, a statistical analyzer 208, a preprocessor 210, and data
stored in containers (e.g., databases). The data includes a raw
dataset 212, a preprocessed dataset 214, and a statistics
repository 216. It will be appreciated that although the elements
are shown individually, any of the individual elements may be
combined into a single element for performing the functions of each
of the combined elements.
[0037] The client device 202 may have multiple applications
installed on it, including an application for processing data from
datasets. A user of client device 202 may desire or need to review
or process a large raw dataset 212. In order to make sense of the
large amount of data in raw dataset 212, which includes OLAP data
in this example, a user of client device 202 (who may not even be
familiar with the type of data) may request that a visualization
generator 204 assist the user in understanding the data in raw
dataset 212. The visualization generator 204 may then request that
the statistical ranker 206 provide it with a specified number of
smaller subsets of the raw dataset 212 according to a score
associated with each subset via which the subsets may be ranked for
the presence of non-random structures. The statistical ranker 206
may then request that the statistical analyzer 208 provide it with
the results of a statistical analysis of the raw dataset 212. The
statistical analyzer 208 may then request that the preprocessor 210
process raw dataset 212 to generate a preprocessed dataset 214,
such that applying at least one specified type of statistical
analysis to this preprocessed dataset 214 and applying the same
type of statistical analysis to the original raw dataset 212 yields
the same results. The at least one specified type of statistical
analysis may include, for example, the correlation coefficient for
measuring linear dependency. The preprocessing may be accomplished,
for example, by discarding unusable data and duplicate data. The
preprocessing may include a data modeling component (e.g., type and
hierarchy identification) if the data in the raw dataset 212
database has not already been modeled. Furthermore, the
preprocessing may also include column selection and
aggregation.
[0038] In an example, the preprocessing may provide, for each
measure of the raw dataset 212, discarding the measure based on
more than half of the values of the measure being one of
not-applicable ("NA"), zero or otherwise unusable.
[0039] In an example, the preprocessing may provide for aggregating
over at least one attribute of the raw dataset 212; for example:
aggregating over attributes of the raw dataset 212 containing 99%
of the same value, aggregating over attributes of the raw dataset
212 with a cardinality greater than a threshold value, aggregating
over all of the attributes of the raw dataset 212 in order of
decreasing cardinality until a dataset has a threshold size, and
based on the aggregation type of an attribute of the raw dataset
212 being "Average", storing the "Sum" and "Count" values for
corresponding measures of the attribute.
[0040] In an example, the preprocessing may include data modeling
such as for example: determining hierarchical relationships between
the attributes of the raw dataset 212; determining equivalencies
between the attributes and between the measures of the raw dataset
212; for equivalent attributes of the raw dataset 212, discarding
all but one of the equivalent attributes; and for equivalent
measures of the raw dataset 212, discarding all but one of the
equivalent measures.
[0041] The preprocessed dataset (preprocessed dataset 214) may then
be provided to the statistical analyzer 208 and/or be placed (e.g.,
cached) into the preprocessed dataset 214 database for storage.
[0042] The statistical analyzer 208 may then work directly on the
preprocessed dataset 214 for analysis of the data to find subsets
of data exhibiting non-random patterns. The statistical analyzer
208 may also store any statistical results of the analysis of the
preprocessed dataset 214 in the statistics repository 216, e.g., a
database. The statistics repository 216 may be directly accessed by
the statistical ranker 206, whose output is received by the
visualization generator 204 and possibly by other clients, such as
client device 202.
[0043] In an example, the statistical analyzer 208 may analyze the
preprocessed dataset 214 to identify at least one subset of the
preprocessed dataset 214 that includes a non-random structure by
performing an analysis that includes the at least one specified
statistical analysis, for example, the correlation coefficient.
[0044] In an example, the statistical analyzer 208 may analyze only
the subsets of columns in the preprocessed dataset 214 that consist
of an attribute A and a measure M and the subsets that consist of
two attributes A.sub.1 and A.sub.2 and a measure M.
[0045] In an example, the statistical analyzer 208 may analyze the
preprocessed dataset 214 based on the at least one statistical
analysis which includes an analysis of variance (ANOVA) test, a
statistical model that analyzes the differences between group
means.
[0046] The statistical analyzer 208 may generate a score for each
subset based, for example, on the "effect size" of ANOVA for the
subset then provide the statistical ranker 206 with a group of
identified subsets of preprocessed dataset 214 (e.g., AM or
A.sub.1A.sub.2M) together with the scores generated for each
identified subset of the group.
[0047] The statistical ranker 206 may then rank the group of
subsets for insightfulness and/or interestingness (e.g., presence
of non-random data structures) based on the score generated for
each respective subset and provide the visualization generator 204
with a specified number of ranked subsets for the generation of
insightful or interesting visualizations for the user of client
device 202.
[0048] The visualizations produced by visualization generator 204
may include at least one of: a mark representing the different
values of an attribute, a mark type for each type of data point
representation, a mark color property associated with a measure or
an attribute, a mark size property associated with the value of a
measure, x-axis, y-axis, rows, columns and a visualization type
that determines how the data points are displayed in relation to
each other.
[0049] In an example, the visualization generator 204 may determine
which attribute, from a received data subset of preprocessed
dataset 214, to use with a color property of the visualization
based on attribute hierarchies.
[0050] In an example, the visualization generator 204 may select a
mark type, for example bar or dot, based on the cardinality of the
at least one attribute included in the subset of preprocessed
dataset 214 that is being visualised.
[0051] In an example, the visualization generator 204 may select
which attribute, from a received data subset of preprocessed
dataset 214, to use for the color property based on determining
that an attribute is one of included as a mark and included on an
x-axis with a cardinality less than 10 and/or determining that an
attribute is at a higher hierarchy level than an attribute used as
a mark with a cardinality less than 10.
Example Data Modeling
[0052] If the raw dataset 212 has not been modeled, an example
embodiment includes a data modeling phase, where attribute
hierarchies may be mapped and sets of equivalent attributes and
measures identified.
[0053] First, the attributes of raw dataset 212 may be ordered
based on their cardinality. For each combination of two attributes
the following query may be run:
TABLE-US-00001 SELECT D, D*, count(*) FROM OLAP_CUBE GROUP BY D,
D*
[0054] If a hierarchical relationship is identified, then the
relationship may be inserted into the attribute hierarchy map, see
for example Table 1. In this example D is considered an ancestor of
D* since it includes or summarizes D*.
TABLE-US-00002 TABLE 1 Hierarchical Relationship between D and D* D
D* Count(*) A1 X1 30 A1 X2 50 A2 X3 100 A2 X4 200 A2 X5 300
[0055] If a one-to-one relationship is identified between
dimensions, then example embodiments may select one of the
dimensions and discard the remaining dimensions (that have been
identified as being in a one-to-one relationship with the selected
dimension) since they may be considered as equivalent dimensions to
the selected dimension. See for example Table 2.
TABLE-US-00003 TABLE 2 One-to-one Relationship between D and D* D
D* Count(*) A1 X1 30 A2 X2 100
[0056] One-to-one or hierarchical relationships do not need to be
100% exact. If the result is in within a certain range (e.g.
95-100%), the dimensions may be still considered to be
dependent.
[0057] Next, the algorithm identifies equivalent measures. For all
pairs of measures, it calculates their correlation coefficient. In
an example, if their correlation coefficient is greater than a
user-defined threshold (typically this threshold is around 0.95),
these measures may be considered as equivalent measures. Similarly
to the dimension case, only one of the equivalent measures may be
kept and the others discarded. In an example, the statistical
analysis may be restricted to the remaining measures and
dimensions.
[0058] When calculating the aggregated values of a measure, the
following cases need to be distinguished based on the specified
aggregation type:
[0059] Let M=m.sub.1, . . . m.sub.n be the raw data and agg an
aggregation function. Let M.sub.1:=(m.sub.1, . . . m.sub.i), . . .
M.sub.m:=(m.sub.j, . . . m.sub.n) be subsets of M, such that
M.sub.1 .andgate. . . . M.sub.m=O and M.sub.1 .orgate. . . .
M.sub.m=M.
[0060] Then:
Min(m.sub.1, . . . m.sub.n)=Min(Min(M.sub.1), . . .
Min(M.sub.m))
Max(m.sub.1, . . . m.sub.n)=Max(Max(M.sub.1), . . .
Max(M.sub.m))
Count(m.sub.1, . . . m.sub.n)=Count(M.sub.1)+ . . .
Count(M.sub.m)
Sum(m.sub.1, . . . m.sub.n)=Sum(M.sub.1)+ . . . Sum(M.sub.m)
Avg(m.sub.1, . . . m.sub.n)=(Sum(M.sub.1)+ . . .
Sum(M.sub.m))/(Count(M.sub.1)+ . . . Count(M.sub.m))
[0061] It will, of course, be appreciated that other aggregation
types that have similar behaviour may be processed in a similar
manner.
[0062] Since for some aggregation types, such as the ones mentioned
above, the aggregated values may be correctly calculated based on
the values in the preprocessed dataset 214, the statistical
analysis by statistical analyzer 208 does not require access the
original raw dataset 212 (for these aggregation types).
Example Impact of Aggregation
[0063] FIG. 3A includes a graph 300 illustrating an example of how
aggregation of OLAP data may change the patterns or structures that
are visible in data. This can be important because patterns can
both disappear or appear based on aggregation. For example, in
graph 300 the pattern that is clearly visible in the quarterly
(i.e., raw) data disappears when aggregating over the attribute
"Quarter" to obtain yearly data. The yearly data does not show any
pattern.
[0064] FIG. 3B includes two side-by-side tables 320 and 340
illustrating an example of how aggregation of OLAP data may change
the patterns or structures that are visible in data. In table 320,
there is very little variance in the values of the measure "Salary"
across the attribute "Employees". However, when aggregating over
the attribute "Team" in table 340, using the aggregator "sum" on
the measure "Salary", the values are quite different for each team.
As may be seen in table 340, no team member's salary varies by more
than 20% and yet the aggregate salaries of team A and team B differ
by 200%. Because of the impact that aggregation may have on the
patterns that may be visible (i.e., the non-random patterns that
may be identified), the statistical analysis may be run on the
level of aggregation that will be presented to the user.
Example Visualization Guidelines
[0065] FIG. 4 includes a table 400 illustrating example
visualization guidelines that may be used in accordance with the
systems and methods presented herein. For example, in table 400
visualization guidelines for a subset include two attributes
A.sub.1 and A.sub.2 and a measure M.
[0066] As can be seen in table 400, the x-axis is used for
attribute A.sub.1, the y-axis is used for measure M, and the
attribute A.sub.2 is used for the "mark".
[0067] In an example, choosing a mark type is dependent on the
cardinality of the attributes included in the visualization. For
example, in FIG. 4 it is specified that if A.sub.2 is less than 5
than the mark type should be "Text".
[0068] In an example, the mark type may be considered when choosing
an attribute or measure for the color property of the
visualization. This can be seen in table 400 which provides that
attribute A.sub.2 should be used with the color property if the
mark type is "Text", i.e., "If Text, A.sub.2".
[0069] In an example, columns may be used with the color property
only if they are attribute columns with cardinality less than 10.
Therefore attribute data may be considered when choosing an
attribute for the color property. For example, if an attribute is
included as the "mark" and has cardinality less than 10, then this
attribute may be considered for the color property. If these
conditions (i.e. attribute is mark and has cardinality <10) are
not present, then if an attribute on the x-axis has cardinality
less than 10, this attribute should be chosen for the color
property. If these conditions (i.e. attribute is mark and has
cardinality <10) are not present, then the visualization
guidelines include searching up the attribute hierarchy of the
attribute used as the "mark", until an attribute is reached with
cardinality less than 10; this attribute should then be chosen for
the color property. If these conditions are not present, then the
visualization guidelines don't suggest color in the
visualization.
[0070] In an example, the type of visualization (e.g., comparison,
stack, spread, group) may be based on the cardinality of
attributes. For example, if A.sub.2>2 then visualization type 1
(e.g., spread) or if A.sub.1<=10 then visualization type 2.
[0071] In an example, the subset data may be sorted based on the
value of the measure included in visualization, e.g., M in table
400.
Example Limitation of Search Space
[0072] FIG. 5 illustrates an example mapping of the hierarchies of
a particular OLAP dataset for the purpose of limiting the search
space of the statistical analysis described herein. As mentioned
above, the search space of the statistical analyzer 208 may, for
example, be restricted to only certain subsets of columns contained
in the preprocessed dataset 214. For example the statistical
analyzer 208 may analyze the subsets of columns that consist of an
attribute A and a measure M and the subsets that consist of two
attributes A.sub.1 and A.sub.2 and a measure M. In this example,
the attribute hierarchies may be used to limit the data search
space for statistical analysis. The a.sub.i-a.sub.j attribute
combinations may be restricted as follows: once the first
attribute, a.sub.i, is fixed, the second, a.sub.j, is selected from
the list of attributes, while ignoring the children of a.sub.i.
Accordingly, in the example of FIG. 5, if a.sub.i=A.sub.3, then
A.sub.4 should not be chosen for a subset combination. Furthermore,
in the example of FIG. 5, if a.sub.i=A.sub.1, then A.sub.2, A.sub.3
and A.sub.4 should not be chosen for a subset combination.
[0073] This is beneficial since the data in the children is
included in and summarized by the parent and may therefore be
duplicative or not provide an interesting visualization. Since
statistical analysis often requires a set of values of at least a
specified size, the above-noted combination selection criteria may
be used to the most appropriate attributes. For example, one of the
assumptions of the ANOVA test is that there are at least five data
points for each value of a.sub.i, and therefore the above-noted
strategy helps in reducing the search space by leaving out
combinations that aren't fulfilling the assumptions of ANOVA.
Example Statistical Analysis And Ranking
[0074] In an example, and as mentioned above, the search space of
the statistical analyzer 208 is restricted to certain subsets of
columns contained in the preprocessed dataset 214, which includes
measures and dimensions. However since the values of measures may
be calculated accurately based on the aggregated data, the
statistical analyzer 208 does not require access to the original
raw dataset 212. This also reduces the runtime since the amount of
data being analyzed may be much less than the amount of data in the
original raw dataset 212.
[0075] In an example, he statistical analyzer 208 may perform
different types of statistical tests on the preprocessed dataset
214 including, for example, ANOVA (Analysis of variance), a
statistical model that analyzes the differences between group
means, and Shapiro-Wilk, a statistical test to test for normality
based on the cardinality of an attribute.
[0076] In an example, and for the purpose of the following pseudo
code, the following four variable names are created:
[0077] A.sub.2-10: includes attributes with cardinality between 2
and 10.
[0078] A.sub.5-10: includes attributes with cardinality between 5
and 10.
[0079] A.sub.10-25: includes attributes with cardinality between 10
and 25.
[0080] A>.sub.25: includes attributes with cardinality >25 in
increasing order.
[0081] In an example, the following pseudo code for using ANOVA may
be used to generate the attribute-attribute-measure combinations
for a subset of preprocessed dataset 214 including two attributes
and a measure. The assumptions for ANOVA include a significance
level as input and, typically, this level has a value between .05
and .01. The significance level may also be adjusted to guarantee a
certain result size.
[0082] Pseudo Code for Run-Time Optimization for A-A-M
Combinations:
TABLE-US-00004 double significanceLevel; For (a.sub.i in
A.sub.2-10) For (a.sub.j in (A.sub.10-25 .nu. A.sub.5-10 .nu.
A.sub.> 25)) // order is important! If (a.sub.i <> a.sub.j
& a.sub.j is not ancestor of a.sub.i) // see Table 1 above for
ancestor example For (m in Measures) //order of measures should be
random Run ANOVA on the following data: SELECT a.sub.i, agg(m) FROM
Preprocessed DataSet GROUP BY a.sub.i, a.sub.j If (p-value <
significanceLevel) Include (a.sub.i, a.sub.j, m) in resultList
If(optimizeRunTime) break
[0083] Assumptions: ANOVA assumes that there are at least five data
points for each value in a.sub.i.
[0084] In an example, the results are ranked based on their effect
size, which is one of the statistics that is a result of ANOVA.
[0085] In an example, the following pseudo code is for using the
Shapiro-Wilk statistical test to generate the
attribute-attribute-measure combinations for a subset of
preprocessed dataset 214 including two attributes and a
measure.
[0086] Pseudo Code for A-M Combinations:
TABLE-US-00005 double significanceLevel; For (a in A> 25) For (m
in Measures) //measures in random order Run Shapiro-Wilk for SELECT
a, agg(m) FROM Preprocessed DataSet GROUP BY a If (p-value <
significanceLevel) Include a-m combination in ResultSet
[0087] Runtime Improvement Strategy: [0088] If (the p-values of the
first six Shapiro-Wilk tests for a are <significanceLevel")
[0089] Stop and rank results for a first visualization scenario
(e.g., spread); [0090] Else use a second visualization scenario
(e.g., group).
[0091] Example Ranking of Results:
[0092] Attribute-measure combinations may be ranked by the
statistical ranker 206 based on the following score that is based
on the skew and excess kurtosis of the underlying data:
EffectSize=normalizedSkew+normalizedKurtosis. [0093]
normalizedSkew:=divide skew values by standard deviation; and take
absolute value. [0094] normalizedKurtosis:=divide positive skew
values by their standard deviation; divide negative skew values by
their standard deviation; and take absolute value.
[0095] Of course, other scoring functions may also be used in
addition to the above or as an alternative.
[0096] Combining Results:
[0097] If the algorithm returns more than one a-m combination for
an attribute a, then up to three a-m, a-m' combinations may be
provided to the visualization generator 204 in order of their
ranking
Example Methods
[0098] Further methods will be described below; in particular the
methods will be described in relation to the previously described
figures and elements.
[0099] FIG. 6 is a flowchart illustrating a method 600 for
visualizing the data in a dataset, according to an example
embodiment. In an example, the method 600 may include operations
for: receiving a dataset and generating a preprocessed dataset from
the dataset at operation 610, identifying subsets of the
preprocessed dataset that include non-random structure at operation
620; generating a score for each subset at operation 630; selecting
a subset based on its associated score at operation 640; and
generating a visualization of the selected subset at operation
650.
[0100] In an example, the method 600 may begin at operation 610
with the raw dataset 212 being accessed by a preprocessor 210 which
processes the raw dataset to generate a preprocessed dataset 214
from the raw dataset 212. The processing includes applying at least
one algorithm to the raw dataset 212 to produce preprocessed
dataset 214 such that at least one specified type of statistical
analysis produces the same results when applied to each of the
datasets 212 and 214. In certain examples, the preprocessor 210 may
discard useless data and/or duplicative data to generate the
preprocessed dataset 214.
[0101] At operation 620, the method 600 may continue with the
statistical analyzer 208 analyzing the preprocessed dataset 214
based, at least in part, on the at least one specified type of
statistical analysis, in order to identify subsets of the
preprocessed dataset 214 that include data that exhibits non-random
patterns. For example, the data of the preprocessed dataset 214 may
be subjected to an ANOVA or Shapiro-Wilk analysis in order to
identify potentially interesting subsets of the preprocessed
dataset 214. At operation 630, the method 600 may continue with the
statistical analyzer 208 generating a score for each subset of the
preprocessed dataset 214 based on the analysis of the preprocessed
dataset 214. In certain examples, this may be based on the "effect
size" of each subset. At operation 640, the statistical ranker 206
may rank the subsets according to their associated score and
forward a specified number of the highest scoring subsets to the
visualization generator 204 for display. Finally, at operation 650,
the method 600 may include the visualization generator 204
generating a visualization for each received subset where each
visualization highlights the non-random patterns in the data of
each of the subsets being visualized. In certain examples this may
include using color or size to emphasize the identified patterns
that will be presented to the user.
[0102] FIG. 7 is a flowchart illustrating a method 700 for
visualizing the data in a dataset, according to an example
embodiment. In an example, the method 700 may include operations
for: identifying data types and hierarchies of a dataset including
measures and dimensions at operation 710; aggregating over
attributes of the dataset at operation 720; discarding useless or
duplicative measures and attributes at operation 730; analyzing
only certain subsets of the preprocessed dataset at operation 740;
generating a score for each subset and selecting a subset of the
preprocessed dataset based on this score at operation 750; and
generating a visualization that highlights the non-random structure
of the selected subset at operation 760.
[0103] In an example, the method 700 may begin at operation 710
with the raw dataset 212, which includes measures and dimensions
(e.g., OLAP data or table data) being accessed by a preprocessor
210 which identifies data types of the raw dataset and models the
data of the raw dataset 212, e.g., to determine hierarchies. The
preprocessor 210, at operation 720, aggregates over selected
attributes of the raw dataset 212. At operation 730, the method 700
continues with the preprocessor 210 discarding any useless data
(e.g., with a zero value) and/or duplicative data (e.g., equivalent
dimensions of the cube) of the raw dataset 212 to generate the
preprocessed dataset 214. At operation 740, the method 700 may
continue with the statistical analyzer 208 analyzing preprocessed
dataset 214 by analyzing only those subsets of the preprocessed
dataset 214 consisting of an attribute and a measure and those
subsets consisting of two attributes and a measure. For example,
the data of the preprocessed dataset 214 may be subjected to an
ANOVA or Shapiro-Wilk analysis in order to identify potentially
interesting subsets of the preprocessed dataset 214. At operation
750, the method 700 may continue with the statistical analyzer 208
generating a score for each subset of the preprocessed dataset 214
based on the analysis of the preprocessed dataset 214. At operation
760, the statistical ranker 206 may rank each subset based on its
associated score and select a specified number of subsets, based on
their ranking, to forward to the visualization generator 204 for
display in a manner highlights the non-random patterns in the data
of each of the subsets being visualized.
Modules, Components and Logic
[0104] Certain embodiments are described herein as including logic
or a number of components, modules, or mechanisms. Modules may
constitute either software modules (e.g., code embodied on a
machine-readable medium or in a transmission signal) or hardware
modules. A hardware module is a tangible unit capable of performing
certain operations and may be configured or arranged in a certain
manner. In example embodiments, one or more computer systems (e.g.,
a standalone, client, or server computer system) or one or more
hardware modules of a computer system (e.g., a processor or a group
of processors) may be configured by software (e.g., an application
or application portion) as a hardware module that operates to
perform certain operations as described herein.
[0105] In various embodiments, a hardware module may be implemented
mechanically or electronically. For example, a hardware module may
comprise dedicated circuitry or logic that is permanently
configured (e.g., as a special-purpose processor, such as a field
programmable gate array (FPGA) or an application-specific
integrated circuit (ASIC)) to perform certain operations. A
hardware module may also comprise programmable logic or circuitry
(e.g., as encompassed within a general-purpose processor or other
programmable processor) that is temporarily configured by software
to perform certain operations. It will be appreciated that the
decision to implement a hardware module mechanically, in dedicated
and permanently configured circuitry, or in temporarily configured
circuitry (e.g., configured by software) may be driven by cost and
time considerations.
[0106] Accordingly, the term "hardware module" should be understood
to encompass a tangible entity, be that an entity that is
physically constructed, permanently configured (e.g., hardwired) or
temporarily configured (e.g., programmed) to operate in a certain
manner and/or to perform certain operations described herein.
Considering embodiments in which hardware modules are temporarily
configured (e.g., programmed), each of the hardware modules need
not be configured or instantiated at any one instance in time. For
example, where the hardware modules comprise a general-purpose
processor configured using software, the general-purpose processor
may be configured as respective different hardware modules at
different times. Software may accordingly configure a processor,
for example, to constitute a particular hardware module at one
instance of time and to constitute a different hardware module at a
different instance of time.
[0107] Hardware modules may provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules may be regarded as being communicatively
coupled. Where multiple of such hardware modules exist
contemporaneously, communications may be achieved through signal
transmission (e.g., over appropriate circuits and buses) that
connects the hardware modules. In embodiments in which multiple
hardware modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module may perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output devices and may operate on a resource (e.g., a
collection of information).
[0108] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented modules that operate to perform one or more
operations or functions. The modules referred to herein may, in
some example embodiments, comprise processor-implemented
modules.
[0109] Similarly, the methods described herein may be at least
partially processor-implemented. For example, at least some of the
operations of a method may be performed by one or more processors
or processor-implemented modules. The performance of certain of the
operations may be distributed among the one or more processors, not
only residing within a single machine, but deployed across a number
of machines. In some example embodiments, the processor or
processors may be located in a single location (e.g., within a home
environment, an office environment or as a server farm), while in
other embodiments the processors may be distributed across a number
of locations.
[0110] The one or more processors may operate to support
performance of the relevant operations in a "cloud computing"
environment or as a "software as a service" (SaaS). Some of the
operations may be performed by a group of computers (e.g., machines
including processors), these operations being accessible via a
network (e.g., the Internet) and via one or more appropriate
interfaces (e.g., APIs).
Electronic Apparatus And System
[0111] Example embodiments may be implemented in digital electronic
circuitry, or in computer hardware, firmware, software, or in
combinations of these. Example embodiments may be implemented using
a computer program product, for example, a computer program
tangibly embodied in an information carrier, for example, in a
machine-readable medium for execution by, or to control the
operation of, data processing apparatus, for example, a
programmable processor, a computer, or multiple computers.
[0112] A computer program may be written in any form of programming
language, including compiled or interpreted languages, and it may
be deployed in any form, including as a stand-alone program or as a
module, subroutine, or other unit suitable for use in a computing
environment. A computer program may be deployed to be executed on
one computer or on multiple computers at one site or distributed
across multiple sites and interconnected by a communication
network.
[0113] In example embodiments, operations may be performed by one
or more programmable processors executing a computer program to
perform functions by operating on input data and generating output.
Method operations may also be performed by, and apparatus of
example embodiments may be implemented as, special purpose logic
circuitry (e.g., a FPGA or an ASIC).
[0114] The computing system may include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. In embodiments deploying
a programmable computing system, it will be appreciated that both
hardware and software architectures may be considered.
Specifically, it will be appreciated that the choice of whether to
implement certain functionality in permanently configured hardware
(e.g., an ASIC), in temporarily configured hardware (e.g., a
combination of software and a programmable processor), or a
combination of permanently and temporarily configured hardware may
be a design choice. Below are set out hardware (e.g., machine) and
software architectures deployed, in example embodiments.
Example Architecture and Machine-Readable Medium
[0115] FIG. 8 is a block diagram of a machine in the example form
of a computer system 800 within which instructions 824 may be
executed for causing the machine to perform any one or more of the
methodologies discussed herein. In alternative embodiments, the
machine operates as a standalone device or may be connected (e.g.,
networked) to other machines. In a networked deployment, the
machine may operate in the capacity of a server or a client machine
in a server-client network environment, or as a peer machine in a
peer-to-peer (or distributed) network environment. The machine may
be a personal computer (PC), a tablet PC, a set-top box (STB), a
PDA, a cellular telephone, a web appliance, a network router,
switch or bridge, or any machine capable of executing instructions
(sequential or otherwise) that specify actions to be taken by that
machine. Further, while only a single machine is illustrated, the
term "machine" shall also be taken to include any collection of
machines that individually or jointly execute a set (or multiple
sets) of instructions to perform any one or more of the
methodologies discussed herein.
[0116] The example computer system 800 includes a processor 802
(e.g., a central processing unit (CPU), a graphics processing unit
(GPU) or both), a main memory 804 and a static memory 806, which
communicate with each other via a bus 808. The computer system 800
may further include a video display unit 810 (e.g., a liquid
crystal displays (LCD) or a cathode ray tube (CRT)). The computer
system 800 also includes an alphanumeric input device 812 (e.g., a
keyboard), a cursor control (user interface (UI) navigation) device
814 (e.g., a mouse), a disk drive unit 816, a signal generation
device 818 (e.g., a speaker) and a network interface device
820.
Machine-Readable Medium
[0117] The disk drive unit 816 includes a machine-readable medium
822 on which is stored one or more sets of data structures and
instructions 824 (e.g., software) embodying or used by any one or
more of the methodologies or functions described herein. The
instructions 824 may also reside, completely or at least partially,
within the main memory 804, static memory 806, and/or within the
processor 802 during execution thereof by the computer system 800,
with the main memory 804 and the processor 802 also constituting
machine-readable media.
[0118] While the machine-readable medium 822 is shown in an example
embodiment to be a single medium, the term "machine-readable
medium" may include a single medium or multiple media (e.g., a
centralized or distributed database, and/or associated caches and
servers) that store the one or more instructions 824 or data
structures. The term "machine-readable medium" shall also be taken
to include any tangible medium that is capable of storing, encoding
or carrying instructions (e.g., instructions 824) for execution by
the machine and that cause the machine to perform any one or more
of the methodologies disclosed herein, or that is capable of
storing, encoding or carrying data structures used by or associated
with such instructions. The term "machine-readable medium" shall
accordingly be taken to include, but not be limited to, solid-state
memories and optical and magnetic media. Specific examples of
machine-readable media include non-volatile memory, including by
way of example, semiconductor memory devices (e.g., Erasable
Programmable Read-Only Memory (EPROM), Electrically Erasable
Programmable Read-Only Memory (EEPROM)) and flash memory devices;
magnetic disks such as internal hard disks and removable disks;
magneto-optical disks; and CD-ROM and DVD-ROM disks.
Transmission Medium
[0119] The instructions 824 may further be transmitted or received
over a communications network 826 using a transmission medium. The
instructions 824 may be transmitted using the network interface
device 820 and any one of a number of well-known transfer protocols
(e.g., HTTP). Examples of communication networks include a LAN, a
WAN, the Internet, mobile telephone networks, Plain Old Telephone
(POTS) networks, and wireless data networks (e.g., WiFi and WiMAX
networks). The term "transmission medium" shall be taken to include
any intangible medium that is capable of storing, encoding or
carrying instructions for execution by the machine, and includes
digital or analog communications signals or other intangible media
to facilitate communication of such software.
[0120] Although the present disclosure includes references to
specific example embodiments, it will be evident that various
modifications and changes may be made to these embodiments without
departing from the broader spirit and scope of the present
disclosure. Accordingly, the specification and drawings are to be
regarded in an illustrative rather than a restrictive sense.
[0121] The accompanying drawings that form a part hereof, show by
way of illustration, and not of limitation, specific embodiments in
which the subject matter may be practiced. The embodiments
illustrated are described in sufficient detail to enable those
skilled in the art to practice the teachings disclosed herein.
Other embodiments may be used and derived therefrom, such that
structural and logical substitutions and changes may be made
without departing from the scope of this disclosure. This Detailed
Description, therefore, is not to be taken in a limiting sense, and
the scope of various embodiments is defined only by the appended
claims, along with the full range of equivalents to which such
claims are entitled.
[0122] Thus, although specific embodiments have been illustrated
and described herein, it should be appreciated that any arrangement
calculated to achieve the same purpose may be substituted for the
specific embodiments shown. This disclosure is intended to cover
any and all adaptations or variations of various embodiments.
Combinations of the above embodiments, and other embodiments not
specifically described herein, will be apparent to those of skill
in the art upon reviewing the above description.
[0123] All publications, patents, and patent documents referred to
in this document are incorporated by reference herein in their
entirety, as though individually incorporated by reference. In the
event of inconsistent usages between this document and those
documents so incorporated by reference, the usage in the
incorporated reference(s) should be considered supplementary to
that of this document; for irreconcilable inconsistencies, the
usage in this document controls.
[0124] In this document, the terms "a" or "an" are used, as is
common in patent documents, to include one or more than one,
independent of any other instances or usages of "at least one" or
"one or more." In this document, the term "or" is used to refer to
a nonexclusive or, such that "A or B" includes "A but not B," "B
but not A," and "A and B," unless otherwise indicated. In the
appended claims, the terms "including" and "in which" are used as
the plain-English equivalents of the respective terms "comprising"
and "wherein." Also, in the following claims, the terms "including"
and "comprising" are open-ended; that is, a system, device,
article, or process that includes elements in addition to those
listed after such a term in a claim are still deemed to fall within
the scope of that claim. Moreover, in the following claims, the
terms "first," "second," and "third," and so forth are used merely
as labels, and are not intended to impose numerical requirements on
their objects.
[0125] The Abstract of the Disclosure is provided to comply with 37
C.F.R. .sctn.1.72(b), requiring an abstract that will allow the
reader to quickly ascertain the nature of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. In addition,
in the foregoing Detailed Description, it may be seen that various
features are grouped together in a single embodiment for the
purpose of streamlining the disclosure. This method of disclosure
is not to be interpreted as reflecting an intention that the
claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter lies in less than all features of a single
disclosed embodiment. Thus the following claims are hereby
incorporated into the Detailed Description, with each claim
standing on its own as a separate embodiment.
* * * * *